Breast Cancer Prediction using KNN, SVM, Logistic Regression and Decision Tree

Abstract

Abstract: Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as astrong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), RandomForest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer WisconsinDiagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of thisresearch paperisto predict and diagnosis breast cancer, using machine- learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%). All the work is done in the Anaconda environment based on python programming language and Scikit-learn library. Keywords: Decision Tree, KNN, SVM, Malignant, Benign , Logistic Regression